5 Steps for Crafting Your Data Science Resume

5 steps for crafting your data science resume

Data scientist, quantitative analyst, operations research scientist - whatever you call it (the full list of similar titles seems to expand daily), professionals in these fields are experts at using mathematics and data to solve complex problems, run simulations or build multivariate models. However, when it comes to putting together a high-quality ‘data science resume’, many of these same professionals may need a refresher course. Fortunately, most data science professionals should be familiar with the basic steps below from their work in the classroom or the field.

Framing the Problem

Before you can construct your resume, you first need to get organized. Mostly, this means gathering the necessary documents/information which help you recall and summarize your past work and educational experience. Helpful resources can include copies of past job descriptions, university transcripts, and employer websites for finding location information, examples of how the organization describes its products/services, etc. If you’ve completed any MOOC courses and certifications (like CAP or aCAP), these are also excellent ways to frame yourself as a skilled data scientist.

Organizing the Data

Being concise is an important resume skill. Once you’ve identified your relevant experience in step one, it’s important to think critically about which parts of your experience are most impactful. Most people who read resumes see a considerable number of them daily, so you want yours to stand out without fatiguing the reader. Generally, one page is the recommended length, so you’ll want to filter out any of the extraneous data you’ve gathered. Fortunately, you don’t have to write in complete sentences when describing your experience, so it’s easier to keep the document brief overall.

Standardizing Your Approach

Although there is no right answer when it comes to styling and formatting decisions for your resume, it is important to be consistent. Choose a clear, readable font and use the same font throughout your resume. Also, if you use bold, italicized or underlined text to denote specific sections or pieces of information (i.e. bold text on position title), then you should ensure that every position title on your resume is formatted the same way. Similarly, you should decide if you will list information with bullets, dashes or another type of symbol and keep this consistent as well.  Remember to begin each of your points with a past tense verb (i.e. -Developed a predictive algorithm for monthly revenue).

Building Out Your Model

Being specific about your experience can make all the difference in telling an impactful story about your ability to succeed in a new role. Some details are better than others, and one of the most important things to remember - especially for a data scientist - is the metrics. You need to speak to “how many” projects or “how impactful” your work was (i.e. % increase in revenue). Your experience may not exactly fit this example, but you’ve certainly done things worth quantifying and should put thought into how best to tell that story. You’ll also want to include a “skills” section highlighting any coding languages or tools you’ve used in the past (i.e. R, Python, SQL) and any buzzwords you have associated experience with like ‘Machine Learning’.

Deploying the Model and Managing Lifecycle

It can seem tedious to change your resume for every job application, but a bit of extra effort can go a long way. Seriously. Assuming you’re applying to a certain type of job, data science positions - for instance, there’s no need to reinvent the wheel with each application. Simple changes like switching out words on your resume with synonyms from each position’s job description (i.e. “analytics” to “data science”) are often enough. Depending on the industry you’re seeking work in, it may also make sense to highlight different pieces of your experience more strongly, especially anything specific to that industry. A model can be effective at providing solutions for a particular problem, but it will typically need tweaks or even large-scale changes to be as adept at handling another type of problem. Your resume is the same.

Final Thoughts: A reminder of the most important piece of the resume-building process: your work is never really done. As you gain more experience, you have more data at your disposal. Over time, you should circle back to step one, reframe the problem, and update your model accordingly. Check back with us later to make sure you’re staying up-to-date on the best practices for building your very own data science resume!

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